SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications

By Isabelle Augenstein, Mrinal Das, Sebastian Riedel, Lakshmi Vikraman, Andrew McCallum. International Workshop on Semantic Evaluation, 2017

375 citations recorded by the source corpus.

We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a26accf878216be4388ad9a0474e658aa03d33e2

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Citations in the corpus

A Supervised Approach to Extractive Summarisation of Scientific Papers (2017, methodology): “tion community, for other tasks in the space of automatically understand scientific publications, such as keyphrase extraction (Kim et al., 2010; Sterckx et al., 2016; Augenstein et al., 2017; Augenstein and Søgaard, 2017), semantic relation extraction (Gupta and Manning, 2011; Marsi and”
The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction (2017, positive): “The ScienceIE shared task (Augenstein et al., 2017) focuses on information extraction from scientiﬁc papers.”
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora (2021, background): “…over Synthesis procedures (Mysore et al., 2019) and named entity recognition over material science papers (MNER) (Olivetti et al., 2020) for material science domain; keyphrase classiﬁcation and hyponym classiﬁcation after ﬁltering out physics papers for the physics domain (Augenstein et al., 2017).”
Construction of the Literature Graph in Semantic Scholar (2018, methodology): “We use the standard data splits of the SemEval-2017 Task 10 on entity (and relation) extraction from scientiﬁc papers (Augenstein et al., 2017).”
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction (2018, methodology): “S CI ERC extends previous datasets in scientiﬁc articles SemEval 2017 Task 10 (SemEval 17) (Augenstein et al., 2017) and SemEval 2018 Task 7 (SemEval 18) (G´abor et al., 2018) by extending entity types, relation types, relation coverage, and adding cross-sentence relations using coreference links.”
RadGraph: Extracting Clinical Entities and Relations from Radiology Reports (2021, methodology): “Outside the domain of radiology, several datasets have been speciﬁcally developed for the tasks of entity and relation extraction, such as SciERC [18] and SemEval 2017 Task 10 [19] for scientiﬁc information extraction.”